This study proposes a dynamic filtering based framework aimed at improving the performance and reliability of power grid control systems. To achieve this, several distortion identification techniques are first examined to determine the most efficient method for real-time operation. The selected dynamic filter is subsequently incorporated into a photovoltaic generator (PVG) to support the delivery of high quality electrical energy and to optimize power flow supervision. The obtained results confirm that integrating renewable generation with intelligent energy management significantly enhances grid stability, control accuracy, and overall system efficiency.

Optimization algorithms are a vital tool in many fields, from machine learning and data science to engineering and finance. They allow us to find the best solution to a given problem by searching through a space of possible solutions and selecting the one that maximizes or minimizes a particular objective function. This article is concerned the optimization procedure based on the particle swarm method uses a fitness function as the minimum of the mass of condaction magnetohydrodynamic pump MHD. The Hydrodynamic and thermel model are carried out by the finite volume method. The optimized results of the performance characteristics of the conduction pump are obtained.